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Title: Correcting for dietary measurement error in epidemiology where there is no calibration data
Author: Day, J. G.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2006
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Biomarkers can provide a near error-free assessment of the intake of certain nutrients: for example, protein intake can be inferred from urinary Nitrogen levels and energy intake from Doubly Labelled Water method measurement of energy expenditure. However, historical cohort studies have often not included such calibration methods, and biomarker derived measurements of nutrient intake which are of calibration standard are not available for most nutrients. We develop a Bayesian method for correcting the value of the regression coefficient which measures the strength of the relationship between a disease related response variable and a dietary covariate. Calibration is implemented using informative prior distributions on the value of certain error-related model parameters; these informative priors are inferred from other studies which have included nutrient intake measurements of calibration quality. The model is successfully applied to cross-sectional and longitudinal epidemiological datasets. A limitation of these models is that where measurement error has caused a statistically significant relationship between the response variable and a dietary covariate to appear insignificant, the model is not able to recover the true significance of the relationship, and performs in this respect no better than an ordinary linear regression of the response variable on the uncorrected nutrient intake covariates. We conjecture that this a general limitation of measurement error correcting methods without internal calibration data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available